{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7705823f",
   "metadata": {},
   "source": [
    "# 特征数据质量检查系统\n",
    "\n",
    "本notebook提供特征数据的全面质量检查,包括:\n",
    "- 通用文件加载\n",
    "- 数据质量检查\n",
    "- 特征可视化分析\n",
    "- 特征优化建议\n",
    "\n",
    "适用于所有pkl格式的特征文件。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65eb4f31",
   "metadata": {},
   "source": [
    "## 导入依赖库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7dcf47ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "依赖库加载完成\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "import gc\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from scipy.stats import kstest, shapiro, normaltest\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from collections import defaultdict\n",
    "import json\n",
    "\n",
    "# 设置中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'Arial Unicode MS']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 设置图表样式\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.rcParams['figure.figsize'] = (12, 6)\n",
    "\n",
    "print(\"依赖库加载完成\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c07a44b0",
   "metadata": {},
   "source": [
    "## 1. 通用文件加载函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "488a25d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征加载器已初始化\n"
     ]
    }
   ],
   "source": [
    "class FeatureLoader:\n",
    "    \"\"\"\n",
    "    特征文件加载器\n",
    "    支持批量加载feature目录下的所有pkl文件\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, feature_dir='./feature'):\n",
    "        \"\"\"\n",
    "        初始化加载器\n",
    "        \n",
    "        参数:\n",
    "        - feature_dir: 特征文件目录路径\n",
    "        \"\"\"\n",
    "        self.feature_dir = feature_dir\n",
    "        self.features_dict = {}\n",
    "        self.feature_info = {}\n",
    "        \n",
    "    def load_single_feature(self, file_path):\n",
    "        \"\"\"\n",
    "        加载单个pkl特征文件\n",
    "        \n",
    "        参数:\n",
    "        - file_path: pkl文件路径\n",
    "        \n",
    "        返回:\n",
    "        - DataFrame: 特征数据\n",
    "        \"\"\"\n",
    "        try:\n",
    "            with open(file_path, 'rb') as f:\n",
    "                data = pickle.load(f)\n",
    "            \n",
    "            if not isinstance(data, pd.DataFrame):\n",
    "                raise ValueError(f\"文件 {file_path} 不是DataFrame格式\")\n",
    "            \n",
    "            return data\n",
    "        except Exception as e:\n",
    "            print(f\"加载文件 {file_path} 失败: {str(e)}\")\n",
    "            return None\n",
    "    \n",
    "    def load_all_features(self, pattern='*.pkl'):\n",
    "        \"\"\"\n",
    "        批量加载所有特征文件\n",
    "        \n",
    "        参数:\n",
    "        - pattern: 文件匹配模式\n",
    "        \n",
    "        返回:\n",
    "        - dict: {文件名: DataFrame}\n",
    "        \"\"\"\n",
    "        if not os.path.exists(self.feature_dir):\n",
    "            print(f\"目录不存在: {self.feature_dir}\")\n",
    "            return {}\n",
    "        \n",
    "        pkl_files = [f for f in os.listdir(self.feature_dir) if f.endswith('.pkl')]\n",
    "        \n",
    "        if not pkl_files:\n",
    "            print(f\"未找到pkl文件在目录: {self.feature_dir}\")\n",
    "            return {}\n",
    "        \n",
    "        print(f\"发现 {len(pkl_files)} 个特征文件\")\n",
    "        print(\"=\"*80)\n",
    "        \n",
    "        for pkl_file in pkl_files:\n",
    "            file_path = os.path.join(self.feature_dir, pkl_file)\n",
    "            file_name = os.path.splitext(pkl_file)[0]\n",
    "            \n",
    "            print(f\"\\n正在加载: {pkl_file}\")\n",
    "            data = self.load_single_feature(file_path)\n",
    "            \n",
    "            if data is not None:\n",
    "                self.features_dict[file_name] = data\n",
    "                \n",
    "                # 记录文件信息\n",
    "                self.feature_info[file_name] = {\n",
    "                    'file_path': file_path,\n",
    "                    'file_size_mb': os.path.getsize(file_path) / 1024 / 1024,\n",
    "                    'shape': data.shape,\n",
    "                    'memory_mb': data.memory_usage(deep=True).sum() / 1024 / 1024,\n",
    "                    'columns': data.columns.tolist()\n",
    "                }\n",
    "                \n",
    "                print(f\"  - 形状: {data.shape}\")\n",
    "                print(f\"  - 文件大小: {self.feature_info[file_name]['file_size_mb']:.2f} MB\")\n",
    "                print(f\"  - 内存占用: {self.feature_info[file_name]['memory_mb']:.2f} MB\")\n",
    "        \n",
    "        print(\"\\n\" + \"=\"*80)\n",
    "        print(f\"成功加载 {len(self.features_dict)} 个特征文件\")\n",
    "        \n",
    "        return self.features_dict\n",
    "    \n",
    "    def get_feature_summary(self):\n",
    "        \"\"\"\n",
    "        获取所有特征文件的汇总信息\n",
    "        \n",
    "        返回:\n",
    "        - DataFrame: 汇总表\n",
    "        \"\"\"\n",
    "        if not self.feature_info:\n",
    "            print(\"请先加载特征文件\")\n",
    "            return None\n",
    "        \n",
    "        summary_data = []\n",
    "        for name, info in self.feature_info.items():\n",
    "            summary_data.append({\n",
    "                '特征文件': name,\n",
    "                '样本数': info['shape'][0],\n",
    "                '特征数': info['shape'][1] - 1 if 'CUST_NO' in info['columns'] else info['shape'][1],\n",
    "                '文件大小(MB)': round(info['file_size_mb'], 2),\n",
    "                '内存占用(MB)': round(info['memory_mb'], 2)\n",
    "            })\n",
    "        \n",
    "        summary_df = pd.DataFrame(summary_data)\n",
    "        summary_df = summary_df.sort_values('特征数', ascending=False).reset_index(drop=True)\n",
    "        \n",
    "        # 添加汇总行\n",
    "        total_row = pd.DataFrame([{\n",
    "            '特征文件': '总计',\n",
    "            '样本数': '-',\n",
    "            '特征数': summary_df['特征数'].sum(),\n",
    "            '文件大小(MB)': round(summary_df['文件大小(MB)'].sum(), 2),\n",
    "            '内存占用(MB)': round(summary_df['内存占用(MB)'].sum(), 2)\n",
    "        }])\n",
    "        \n",
    "        summary_df = pd.concat([summary_df, total_row], ignore_index=True)\n",
    "        \n",
    "        return summary_df\n",
    "\n",
    "# 创建加载器实例\n",
    "loader = FeatureLoader(feature_dir='./feature')\n",
    "print(\"特征加载器已初始化\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5db4cd",
   "metadata": {},
   "source": [
    "## 2. 数据质量检查函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "57198b1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "质量检查器已定义\n"
     ]
    }
   ],
   "source": [
    "class FeatureQualityChecker:\n",
    "    \"\"\"\n",
    "    特征质量检查器\n",
    "    全面检查特征数据的质量问题\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, df, name='特征数据', id_col='CUST_NO'):\n",
    "        \"\"\"\n",
    "        初始化检查器\n",
    "        \n",
    "        参数:\n",
    "        - df: DataFrame\n",
    "        - name: 数据集名称\n",
    "        - id_col: ID列名称\n",
    "        \"\"\"\n",
    "        self.df = df\n",
    "        self.name = name\n",
    "        self.id_col = id_col\n",
    "        self.feature_cols = [col for col in df.columns if col != id_col]\n",
    "        self.quality_report = {}\n",
    "        \n",
    "    def check_basic_info(self):\n",
    "        \"\"\"检查基础信息\"\"\"\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(f\"基础信息检查: {self.name}\")\n",
    "        print(f\"{'='*80}\")\n",
    "        \n",
    "        info = {\n",
    "            '样本数': len(self.df),\n",
    "            '特征数': len(self.feature_cols),\n",
    "            '总列数': len(self.df.columns),\n",
    "            '内存占用(MB)': round(self.df.memory_usage(deep=True).sum() / 1024 / 1024, 2)\n",
    "        }\n",
    "        \n",
    "        for key, value in info.items():\n",
    "            print(f\"{key}: {value:,}\")\n",
    "        \n",
    "        self.quality_report['basic_info'] = info\n",
    "        return info\n",
    "    \n",
    "    def check_missing_values(self, threshold=0.5):\n",
    "        \"\"\"\n",
    "        检查缺失值\n",
    "        \n",
    "        参数:\n",
    "        - threshold: 缺失率阈值,超过此值的特征将被标记\n",
    "        \"\"\"\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(f\"缺失值检查\")\n",
    "        print(f\"{'='*80}\")\n",
    "        \n",
    "        missing_stats = []\n",
    "        for col in self.feature_cols:\n",
    "            missing_count = self.df[col].isnull().sum()\n",
    "            missing_rate = missing_count / len(self.df)\n",
    "            \n",
    "            if missing_count > 0:\n",
    "                missing_stats.append({\n",
    "                    '特征': col,\n",
    "                    '缺失数': missing_count,\n",
    "                    '缺失率': round(missing_rate * 100, 2),\n",
    "                    '是否超阈值': '是' if missing_rate > threshold else '否'\n",
    "                })\n",
    "        \n",
    "        if missing_stats:\n",
    "            missing_df = pd.DataFrame(missing_stats).sort_values('缺失率', ascending=False)\n",
    "            print(f\"\\n发现 {len(missing_df)} 个特征存在缺失值\")\n",
    "            print(f\"缺失率 > {threshold*100}% 的特征数: {(missing_df['缺失率'] > threshold*100).sum()}\")\n",
    "            \n",
    "            if len(missing_df) <= 20:\n",
    "                print(\"\\n缺失值详情:\")\n",
    "                print(missing_df.to_string(index=False))\n",
    "            else:\n",
    "                print(f\"\\n缺失率最高的前20个特征:\")\n",
    "                print(missing_df.head(20).to_string(index=False))\n",
    "            \n",
    "            self.quality_report['missing_values'] = missing_df\n",
    "        else:\n",
    "            print(\"\\n无缺失值\")\n",
    "            self.quality_report['missing_values'] = None\n",
    "        \n",
    "        return missing_stats\n",
    "    \n",
    "    def check_constant_features(self):\n",
    "        \"\"\"检查常量特征(方差为0)\"\"\"\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(f\"常量特征检查\")\n",
    "        print(f\"{'='*80}\")\n",
    "        \n",
    "        constant_features = []\n",
    "        for col in self.feature_cols:\n",
    "            if self.df[col].nunique() == 1:\n",
    "                constant_features.append({\n",
    "                    '特征': col,\n",
    "                    '唯一值': self.df[col].iloc[0] if len(self.df) > 0 else None\n",
    "                })\n",
    "        \n",
    "        if constant_features:\n",
    "            const_df = pd.DataFrame(constant_features)\n",
    "            print(f\"\\n发现 {len(const_df)} 个常量特征(建议删除)\")\n",
    "            print(const_df.to_string(index=False))\n",
    "            self.quality_report['constant_features'] = const_df\n",
    "        else:\n",
    "            print(\"\\n无常量特征\")\n",
    "            self.quality_report['constant_features'] = None\n",
    "        \n",
    "        return constant_features\n",
    "    \n",
    "    def check_low_variance_features(self, threshold=0.01):\n",
    "        \"\"\"\n",
    "        检查低方差特征\n",
    "        \n",
    "        参数:\n",
    "        - threshold: 方差阈值\n",
    "        \"\"\"\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(f\"低方差特征检查 (阈值: {threshold})\")\n",
    "        print(f\"{'='*80}\")\n",
    "        \n",
    "        low_var_features = []\n",
    "        for col in self.feature_cols:\n",
    "            try:\n",
    "                # 跳过常量特征\n",
    "                if self.df[col].nunique() == 1:\n",
    "                    continue\n",
    "                \n",
    "                # 计算标准化方差\n",
    "                var = self.df[col].var()\n",
    "                if not np.isnan(var) and not np.isinf(var):\n",
    "                    # 使用min-max标准化后的方差\n",
    "                    col_min = self.df[col].min()\n",
    "                    col_max = self.df[col].max()\n",
    "                    if col_max != col_min:\n",
    "                        normalized_var = var / ((col_max - col_min) ** 2)\n",
    "                        if normalized_var < threshold:\n",
    "                            low_var_features.append({\n",
    "                                '特征': col,\n",
    "                                '方差': round(var, 6),\n",
    "                                '标准化方差': round(normalized_var, 6)\n",
    "                            })\n",
    "            except:\n",
    "                continue\n",
    "        \n",
    "        if low_var_features:\n",
    "            low_var_df = pd.DataFrame(low_var_features).sort_values('标准化方差')\n",
    "            print(f\"\\n发现 {len(low_var_df)} 个低方差特征\")\n",
    "            print(low_var_df.head(20).to_string(index=False))\n",
    "            self.quality_report['low_variance_features'] = low_var_df\n",
    "        else:\n",
    "            print(\"\\n无低方差特征\")\n",
    "            self.quality_report['low_variance_features'] = None\n",
    "        \n",
    "        return low_var_features\n",
    "    \n",
    "    def check_infinite_values(self):\n",
    "        \"\"\"检查无穷值\"\"\"\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(f\"无穷值检查\")\n",
    "        print(f\"{'='*80}\")\n",
    "        \n",
    "        inf_features = []\n",
    "        for col in self.feature_cols:\n",
    "            inf_count = np.isinf(self.df[col]).sum()\n",
    "            if inf_count > 0:\n",
    "                inf_features.append({\n",
    "                    '特征': col,\n",
    "                    '无穷值数量': inf_count,\n",
    "                    '占比(%)': round(inf_count / len(self.df) * 100, 2)\n",
    "                })\n",
    "        \n",
    "        if inf_features:\n",
    "            inf_df = pd.DataFrame(inf_features).sort_values('无穷值数量', ascending=False)\n",
    "            print(f\"\\n发现 {len(inf_df)} 个特征包含无穷值\")\n",
    "            print(inf_df.to_string(index=False))\n",
    "            self.quality_report['infinite_values'] = inf_df\n",
    "        else:\n",
    "            print(\"\\n无无穷值\")\n",
    "            self.quality_report['infinite_values'] = None\n",
    "        \n",
    "        return inf_features\n",
    "    \n",
    "    def check_duplicates(self):\n",
    "        \"\"\"检查重复样本\"\"\"\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(f\"重复样本检查\")\n",
    "        print(f\"{'='*80}\")\n",
    "        \n",
    "        # 检查ID重复\n",
    "        if self.id_col in self.df.columns:\n",
    "            id_duplicates = self.df[self.id_col].duplicated().sum()\n",
    "            print(f\"\\nID列重复数: {id_duplicates}\")\n",
    "            \n",
    "            if id_duplicates > 0:\n",
    "                print(f\"警告: 发现 {id_duplicates} 个重复ID\")\n",
    "        \n",
    "        # 检查完全重复的行\n",
    "        row_duplicates = self.df.duplicated(subset=self.feature_cols).sum()\n",
    "        print(f\"完全重复的样本数: {row_duplicates}\")\n",
    "        \n",
    "        self.quality_report['duplicates'] = {\n",
    "            'id_duplicates': id_duplicates if self.id_col in self.df.columns else 0,\n",
    "            'row_duplicates': row_duplicates\n",
    "        }\n",
    "        \n",
    "        return row_duplicates\n",
    "    \n",
    "    def check_outliers(self, n_std=3):\n",
    "        \"\"\"\n",
    "        检查异常值(基于3-sigma原则)\n",
    "        \n",
    "        参数:\n",
    "        - n_std: 标准差倍数\n",
    "        \"\"\"\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(f\"异常值检查 ({n_std}-sigma原则)\")\n",
    "        print(f\"{'='*80}\")\n",
    "        \n",
    "        outlier_features = []\n",
    "        for col in self.feature_cols:\n",
    "            try:\n",
    "                mean = self.df[col].mean()\n",
    "                std = self.df[col].std()\n",
    "                \n",
    "                if std > 0 and not np.isnan(std):\n",
    "                    outliers = ((self.df[col] - mean).abs() > n_std * std).sum()\n",
    "                    if outliers > 0:\n",
    "                        outlier_features.append({\n",
    "                            '特征': col,\n",
    "                            '异常值数量': outliers,\n",
    "                            '异常值占比(%)': round(outliers / len(self.df) * 100, 2)\n",
    "                        })\n",
    "            except:\n",
    "                continue\n",
    "        \n",
    "        if outlier_features:\n",
    "            outlier_df = pd.DataFrame(outlier_features).sort_values('异常值数量', ascending=False)\n",
    "            print(f\"\\n发现 {len(outlier_df)} 个特征包含异常值(前20个):\")\n",
    "            print(outlier_df.head(20).to_string(index=False))\n",
    "            self.quality_report['outliers'] = outlier_df\n",
    "        else:\n",
    "            print(\"\\n无明显异常值\")\n",
    "            self.quality_report['outliers'] = None\n",
    "        \n",
    "        return outlier_features\n",
    "    \n",
    "    def check_correlation(self, threshold=0.95):\n",
    "        \"\"\"\n",
    "        检查高相关性特征\n",
    "        \n",
    "        参数:\n",
    "        - threshold: 相关系数阈值\n",
    "        \"\"\"\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(f\"高相关性检查 (阈值: {threshold})\")\n",
    "        print(f\"{'='*80}\")\n",
    "        \n",
    "        # 计算相关系数矩阵\n",
    "        numeric_cols = self.df[self.feature_cols].select_dtypes(include=[np.number]).columns\n",
    "        \n",
    "        if len(numeric_cols) < 2:\n",
    "            print(\"\\n数值特征少于2个,跳过相关性检查\")\n",
    "            self.quality_report['high_correlation'] = None\n",
    "            return []\n",
    "        \n",
    "        print(f\"\\n计算 {len(numeric_cols)} 个数值特征的相关系数...\")\n",
    "        corr_matrix = self.df[numeric_cols].corr().abs()\n",
    "        \n",
    "        # 找出高相关性特征对\n",
    "        high_corr_pairs = []\n",
    "        for i in range(len(corr_matrix.columns)):\n",
    "            for j in range(i+1, len(corr_matrix.columns)):\n",
    "                if corr_matrix.iloc[i, j] >= threshold:\n",
    "                    high_corr_pairs.append({\n",
    "                        '特征1': corr_matrix.columns[i],\n",
    "                        '特征2': corr_matrix.columns[j],\n",
    "                        '相关系数': round(corr_matrix.iloc[i, j], 4)\n",
    "                    })\n",
    "        \n",
    "        if high_corr_pairs:\n",
    "            corr_df = pd.DataFrame(high_corr_pairs).sort_values('相关系数', ascending=False)\n",
    "            print(f\"\\n发现 {len(corr_df)} 对高相关性特征(前20对):\")\n",
    "            print(corr_df.head(20).to_string(index=False))\n",
    "            self.quality_report['high_correlation'] = corr_df\n",
    "        else:\n",
    "            print(f\"\\n无相关系数 >= {threshold} 的特征对\")\n",
    "            self.quality_report['high_correlation'] = None\n",
    "        \n",
    "        return high_corr_pairs\n",
    "    \n",
    "    def run_all_checks(self):\n",
    "        \"\"\"运行所有质量检查\"\"\"\n",
    "        print(f\"\\n\\n{'#'*80}\")\n",
    "        print(f\"# 特征质量全面检查: {self.name}\")\n",
    "        print(f\"{'#'*80}\\n\")\n",
    "        \n",
    "        self.check_basic_info()\n",
    "        self.check_missing_values()\n",
    "        self.check_constant_features()\n",
    "        self.check_low_variance_features()\n",
    "        self.check_infinite_values()\n",
    "        self.check_duplicates()\n",
    "        self.check_outliers()\n",
    "        self.check_correlation()\n",
    "        \n",
    "        print(f\"\\n{'#'*80}\")\n",
    "        print(f\"# 质量检查完成\")\n",
    "        print(f\"{'#'*80}\\n\")\n",
    "        \n",
    "        return self.quality_report\n",
    "    \n",
    "    def get_summary_report(self):\n",
    "        \"\"\"生成质量检查摘要报告\"\"\"\n",
    "        if not self.quality_report:\n",
    "            print(\"请先运行质量检查\")\n",
    "            return None\n",
    "        \n",
    "        summary = {\n",
    "            '数据集名称': self.name,\n",
    "            '样本数': self.quality_report.get('basic_info', {}).get('样本数', 0),\n",
    "            '特征数': self.quality_report.get('basic_info', {}).get('特征数', 0),\n",
    "            '缺失特征数': len(self.quality_report.get('missing_values', [])) if self.quality_report.get('missing_values') is not None else 0,\n",
    "            '常量特征数': len(self.quality_report.get('constant_features', [])) if self.quality_report.get('constant_features') is not None else 0,\n",
    "            '低方差特征数': len(self.quality_report.get('low_variance_features', [])) if self.quality_report.get('low_variance_features') is not None else 0,\n",
    "            '无穷值特征数': len(self.quality_report.get('infinite_values', [])) if self.quality_report.get('infinite_values') is not None else 0,\n",
    "            '高相关对数': len(self.quality_report.get('high_correlation', [])) if self.quality_report.get('high_correlation') is not None else 0\n",
    "        }\n",
    "        \n",
    "        return summary\n",
    "\n",
    "print(\"质量检查器已定义\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7950d98",
   "metadata": {},
   "source": [
    "## 3. 特征可视化函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "17e4b6eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "可视化工具已定义\n"
     ]
    }
   ],
   "source": [
    "class FeatureVisualizer:\n",
    "    \"\"\"\n",
    "    特征可视化工具\n",
    "    提供多种可视化方法分析特征分布和质量\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, df, name='特征数据', id_col='CUST_NO'):\n",
    "        \"\"\"\n",
    "        初始化可视化器\n",
    "        \n",
    "        参数:\n",
    "        - df: DataFrame\n",
    "        - name: 数据集名称\n",
    "        - id_col: ID列名称\n",
    "        \"\"\"\n",
    "        self.df = df\n",
    "        self.name = name\n",
    "        self.id_col = id_col\n",
    "        self.feature_cols = [col for col in df.columns if col != id_col]\n",
    "    \n",
    "    def plot_missing_values(self, top_n=30):\n",
    "        \"\"\"\n",
    "        可视化缺失值分布\n",
    "        \n",
    "        参数:\n",
    "        - top_n: 显示前N个缺失最多的特征\n",
    "        \"\"\"\n",
    "        missing_data = []\n",
    "        for col in self.feature_cols:\n",
    "            missing_count = self.df[col].isnull().sum()\n",
    "            if missing_count > 0:\n",
    "                missing_data.append({\n",
    "                    'feature': col,\n",
    "                    'missing_count': missing_count,\n",
    "                    'missing_rate': missing_count / len(self.df) * 100\n",
    "                })\n",
    "        \n",
    "        if not missing_data:\n",
    "            print(\"无缺失值,跳过可视化\")\n",
    "            return\n",
    "        \n",
    "        missing_df = pd.DataFrame(missing_data).sort_values('missing_rate', ascending=False).head(top_n)\n",
    "        \n",
    "        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
    "        \n",
    "        # 柱状图\n",
    "        ax1.barh(range(len(missing_df)), missing_df['missing_rate'], color='coral')\n",
    "        ax1.set_yticks(range(len(missing_df)))\n",
    "        ax1.set_yticklabels(missing_df['feature'], fontsize=9)\n",
    "        ax1.set_xlabel('缺失率 (%)', fontsize=11)\n",
    "        ax1.set_title(f'缺失率最高的{top_n}个特征', fontsize=12, fontweight='bold')\n",
    "        ax1.grid(axis='x', alpha=0.3)\n",
    "        \n",
    "        # 饼图 - 缺失值分布\n",
    "        total_missing = missing_df['missing_count'].sum()\n",
    "        top5 = missing_df.head(5)\n",
    "        other = missing_df.iloc[5:]['missing_count'].sum() if len(missing_df) > 5 else 0\n",
    "        \n",
    "        pie_data = list(top5['missing_count'])\n",
    "        pie_labels = list(top5['feature'])\n",
    "        if other > 0:\n",
    "            pie_data.append(other)\n",
    "            pie_labels.append('其他')\n",
    "        \n",
    "        ax2.pie(pie_data, labels=pie_labels, autopct='%1.1f%%', startangle=90)\n",
    "        ax2.set_title('缺失值分布(Top5+其他)', fontsize=12, fontweight='bold')\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "    def plot_feature_distribution(self, features=None, n_cols=4, sample_size=None):\n",
    "        \"\"\"\n",
    "        可视化特征分布\n",
    "        \n",
    "        参数:\n",
    "        - features: 要可视化的特征列表,None则随机选择\n",
    "        - n_cols: 每行显示的图数量\n",
    "        - sample_size: 采样大小,None则使用全部数据\n",
    "        \"\"\"\n",
    "        if features is None:\n",
    "            # 随机选择12个特征\n",
    "            features = np.random.choice(self.feature_cols, min(12, len(self.feature_cols)), replace=False)\n",
    "        \n",
    "        if sample_size and sample_size < len(self.df):\n",
    "            df_sample = self.df.sample(n=sample_size, random_state=42)\n",
    "        else:\n",
    "            df_sample = self.df\n",
    "        \n",
    "        n_features = len(features)\n",
    "        n_rows = (n_features + n_cols - 1) // n_cols\n",
    "        \n",
    "        fig, axes = plt.subplots(n_rows, n_cols, figsize=(n_cols*4, n_rows*3))\n",
    "        axes = axes.flatten() if n_features > 1 else [axes]\n",
    "        \n",
    "        for idx, feature in enumerate(features):\n",
    "            ax = axes[idx]\n",
    "            data = df_sample[feature].dropna()\n",
    "            \n",
    "            if len(data) == 0:\n",
    "                ax.text(0.5, 0.5, '无有效数据', ha='center', va='center', fontsize=12)\n",
    "                ax.set_title(feature, fontsize=10)\n",
    "                continue\n",
    "            \n",
    "            # 绘制直方图和KDE\n",
    "            ax.hist(data, bins=50, alpha=0.6, color='skyblue', edgecolor='black', density=True)\n",
    "            \n",
    "            try:\n",
    "                data.plot(kind='kde', ax=ax, color='red', linewidth=2)\n",
    "            except:\n",
    "                pass\n",
    "            \n",
    "            ax.set_title(f'{feature}\\n(均值:{data.mean():.2f}, 标准差:{data.std():.2f})', \n",
    "                        fontsize=9)\n",
    "            ax.set_xlabel('')\n",
    "            ax.grid(alpha=0.3)\n",
    "        \n",
    "        # 隐藏多余的子图\n",
    "        for idx in range(n_features, len(axes)):\n",
    "            axes[idx].axis('off')\n",
    "        \n",
    "        plt.suptitle(f'{self.name} - 特征分布可视化', fontsize=14, fontweight='bold', y=1.02)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "    def plot_correlation_heatmap(self, top_n=30, method='pearson'):\n",
    "        \"\"\"\n",
    "        绘制相关性热力图\n",
    "        \n",
    "        参数:\n",
    "        - top_n: 显示前N个特征\n",
    "        - method: 相关系数计算方法 ('pearson', 'spearman')\n",
    "        \"\"\"\n",
    "        numeric_cols = self.df[self.feature_cols].select_dtypes(include=[np.number]).columns\n",
    "        \n",
    "        if len(numeric_cols) < 2:\n",
    "            print(\"数值特征少于2个,无法绘制相关性热力图\")\n",
    "            return\n",
    "        \n",
    "        # 选择前N个特征\n",
    "        selected_cols = numeric_cols[:min(top_n, len(numeric_cols))]\n",
    "        \n",
    "        print(f\"计算 {len(selected_cols)} 个特征的相关系数...\")\n",
    "        corr_matrix = self.df[selected_cols].corr(method=method)\n",
    "        \n",
    "        plt.figure(figsize=(14, 12))\n",
    "        sns.heatmap(corr_matrix, annot=False, cmap='coolwarm', center=0,\n",
    "                   square=True, linewidths=0.5, cbar_kws={\"shrink\": 0.8})\n",
    "        plt.title(f'{self.name} - 特征相关性热力图 (前{len(selected_cols)}个特征)', \n",
    "                 fontsize=14, fontweight='bold', pad=20)\n",
    "        plt.xticks(rotation=45, ha='right', fontsize=8)\n",
    "        plt.yticks(fontsize=8)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "    def plot_outlier_boxplot(self, features=None, n_cols=4):\n",
    "        \"\"\"\n",
    "        绘制箱线图检测异常值\n",
    "        \n",
    "        参数:\n",
    "        - features: 要可视化的特征列表\n",
    "        - n_cols: 每行显示的图数量\n",
    "        \"\"\"\n",
    "        if features is None:\n",
    "            # 随机选择12个特征\n",
    "            numeric_cols = self.df[self.feature_cols].select_dtypes(include=[np.number]).columns\n",
    "            features = np.random.choice(numeric_cols, min(12, len(numeric_cols)), replace=False)\n",
    "        \n",
    "        n_features = len(features)\n",
    "        n_rows = (n_features + n_cols - 1) // n_cols\n",
    "        \n",
    "        fig, axes = plt.subplots(n_rows, n_cols, figsize=(n_cols*4, n_rows*3))\n",
    "        axes = axes.flatten() if n_features > 1 else [axes]\n",
    "        \n",
    "        for idx, feature in enumerate(features):\n",
    "            ax = axes[idx]\n",
    "            data = self.df[feature].dropna()\n",
    "            \n",
    "            if len(data) == 0:\n",
    "                ax.text(0.5, 0.5, '无有效数据', ha='center', va='center', fontsize=12)\n",
    "                ax.set_title(feature, fontsize=10)\n",
    "                continue\n",
    "            \n",
    "            bp = ax.boxplot(data, vert=True, patch_artist=True)\n",
    "            bp['boxes'][0].set_facecolor('lightblue')\n",
    "            bp['medians'][0].set_color('red')\n",
    "            bp['medians'][0].set_linewidth(2)\n",
    "            \n",
    "            ax.set_title(feature, fontsize=10)\n",
    "            ax.grid(axis='y', alpha=0.3)\n",
    "        \n",
    "        # 隐藏多余的子图\n",
    "        for idx in range(n_features, len(axes)):\n",
    "            axes[idx].axis('off')\n",
    "        \n",
    "        plt.suptitle(f'{self.name} - 箱线图(异常值检测)', fontsize=14, fontweight='bold', y=1.02)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "    def plot_feature_importance_by_variance(self, top_n=30):\n",
    "        \"\"\"\n",
    "        基于方差绘制特征重要性\n",
    "        \n",
    "        参数:\n",
    "        - top_n: 显示前N个特征\n",
    "        \"\"\"\n",
    "        numeric_cols = self.df[self.feature_cols].select_dtypes(include=[np.number]).columns\n",
    "        \n",
    "        var_data = []\n",
    "        for col in numeric_cols:\n",
    "            var = self.df[col].var()\n",
    "            if not np.isnan(var) and not np.isinf(var):\n",
    "                var_data.append({\n",
    "                    'feature': col,\n",
    "                    'variance': var,\n",
    "                    'std': np.sqrt(var)\n",
    "                })\n",
    "        \n",
    "        if not var_data:\n",
    "            print(\"无有效方差数据\")\n",
    "            return\n",
    "        \n",
    "        var_df = pd.DataFrame(var_data).sort_values('variance', ascending=False).head(top_n)\n",
    "        \n",
    "        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
    "        \n",
    "        # 方差柱状图\n",
    "        ax1.barh(range(len(var_df)), var_df['variance'], color='steelblue')\n",
    "        ax1.set_yticks(range(len(var_df)))\n",
    "        ax1.set_yticklabels(var_df['feature'], fontsize=9)\n",
    "        ax1.set_xlabel('方差', fontsize=11)\n",
    "        ax1.set_title(f'方差最大的{top_n}个特征', fontsize=12, fontweight='bold')\n",
    "        ax1.grid(axis='x', alpha=0.3)\n",
    "        \n",
    "        # 标准差柱状图\n",
    "        ax2.barh(range(len(var_df)), var_df['std'], color='coral')\n",
    "        ax2.set_yticks(range(len(var_df)))\n",
    "        ax2.set_yticklabels(var_df['feature'], fontsize=9)\n",
    "        ax2.set_xlabel('标准差', fontsize=11)\n",
    "        ax2.set_title(f'标准差最大的{top_n}个特征', fontsize=12, fontweight='bold')\n",
    "        ax2.grid(axis='x', alpha=0.3)\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "    \n",
    "    def plot_quality_summary(self, quality_report):\n",
    "        \"\"\"\n",
    "        绘制质量检查摘要图\n",
    "        \n",
    "        参数:\n",
    "        - quality_report: FeatureQualityChecker的质量报告\n",
    "        \"\"\"\n",
    "        fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "        \n",
    "        # 1. 问题特征统计\n",
    "        ax1 = axes[0, 0]\n",
    "        categories = ['缺失值', '常量', '低方差', '无穷值']\n",
    "        counts = [\n",
    "            len(quality_report.get('missing_values', [])) if quality_report.get('missing_values') is not None else 0,\n",
    "            len(quality_report.get('constant_features', [])) if quality_report.get('constant_features') is not None else 0,\n",
    "            len(quality_report.get('low_variance_features', [])) if quality_report.get('low_variance_features') is not None else 0,\n",
    "            len(quality_report.get('infinite_values', [])) if quality_report.get('infinite_values') is not None else 0\n",
    "        ]\n",
    "        colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#FFA07A']\n",
    "        bars = ax1.bar(categories, counts, color=colors, alpha=0.7, edgecolor='black')\n",
    "        ax1.set_ylabel('特征数量', fontsize=11)\n",
    "        ax1.set_title('问题特征统计', fontsize=12, fontweight='bold')\n",
    "        ax1.grid(axis='y', alpha=0.3)\n",
    "        \n",
    "        # 在柱状图上标注数值\n",
    "        for bar in bars:\n",
    "            height = bar.get_height()\n",
    "            if height > 0:\n",
    "                ax1.text(bar.get_x() + bar.get_width()/2., height,\n",
    "                        f'{int(height)}',\n",
    "                        ha='center', va='bottom', fontsize=10, fontweight='bold')\n",
    "        \n",
    "        # 2. 缺失率分布\n",
    "        ax2 = axes[0, 1]\n",
    "        if quality_report.get('missing_values') is not None and len(quality_report['missing_values']) > 0:\n",
    "            missing_df = quality_report['missing_values']\n",
    "            bins = [0, 10, 20, 30, 40, 50, 100]\n",
    "            missing_df['bin'] = pd.cut(missing_df['缺失率'], bins=bins, \n",
    "                                       labels=['0-10%', '10-20%', '20-30%', '30-40%', '40-50%', '50-100%'])\n",
    "            bin_counts = missing_df['bin'].value_counts().sort_index()\n",
    "            ax2.bar(range(len(bin_counts)), bin_counts.values, color='coral', alpha=0.7, edgecolor='black')\n",
    "            ax2.set_xticks(range(len(bin_counts)))\n",
    "            ax2.set_xticklabels(bin_counts.index, rotation=45, ha='right')\n",
    "            ax2.set_ylabel('特征数量', fontsize=11)\n",
    "            ax2.set_title('缺失率分布', fontsize=12, fontweight='bold')\n",
    "            ax2.grid(axis='y', alpha=0.3)\n",
    "        else:\n",
    "            ax2.text(0.5, 0.5, '无缺失值', ha='center', va='center', fontsize=14)\n",
    "            ax2.set_title('缺失率分布', fontsize=12, fontweight='bold')\n",
    "        \n",
    "        # 3. 异常值分布\n",
    "        ax3 = axes[1, 0]\n",
    "        if quality_report.get('outliers') is not None and len(quality_report['outliers']) > 0:\n",
    "            outlier_df = quality_report['outliers'].head(20)\n",
    "            ax3.barh(range(len(outlier_df)), outlier_df['异常值占比(%)'], color='orange', alpha=0.7, edgecolor='black')\n",
    "            ax3.set_yticks(range(len(outlier_df)))\n",
    "            ax3.set_yticklabels(outlier_df['特征'], fontsize=8)\n",
    "            ax3.set_xlabel('异常值占比 (%)', fontsize=11)\n",
    "            ax3.set_title('异常值占比最高的20个特征', fontsize=12, fontweight='bold')\n",
    "            ax3.grid(axis='x', alpha=0.3)\n",
    "        else:\n",
    "            ax3.text(0.5, 0.5, '无异常值', ha='center', va='center', fontsize=14)\n",
    "            ax3.set_title('异常值分布', fontsize=12, fontweight='bold')\n",
    "        \n",
    "        # 4. 高相关性统计\n",
    "        ax4 = axes[1, 1]\n",
    "        if quality_report.get('high_correlation') is not None and len(quality_report['high_correlation']) > 0:\n",
    "            corr_df = quality_report['high_correlation']\n",
    "            corr_bins = [0.90, 0.95, 0.97, 0.99, 1.0]\n",
    "            corr_df['bin'] = pd.cut(corr_df['相关系数'], bins=corr_bins, \n",
    "                                    labels=['0.90-0.95', '0.95-0.97', '0.97-0.99', '0.99-1.0'])\n",
    "            bin_counts = corr_df['bin'].value_counts().sort_index()\n",
    "            ax4.bar(range(len(bin_counts)), bin_counts.values, color='purple', alpha=0.7, edgecolor='black')\n",
    "            ax4.set_xticks(range(len(bin_counts)))\n",
    "            ax4.set_xticklabels(bin_counts.index, rotation=45, ha='right')\n",
    "            ax4.set_ylabel('特征对数量', fontsize=11)\n",
    "            ax4.set_title('高相关性分布', fontsize=12, fontweight='bold')\n",
    "            ax4.grid(axis='y', alpha=0.3)\n",
    "        else:\n",
    "            ax4.text(0.5, 0.5, '无高相关特征对', ha='center', va='center', fontsize=14)\n",
    "            ax4.set_title('高相关性分布', fontsize=12, fontweight='bold')\n",
    "        \n",
    "        plt.suptitle(f'{self.name} - 质量检查摘要', fontsize=16, fontweight='bold', y=1.00)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "print(\"可视化工具已定义\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f58f92",
   "metadata": {},
   "source": [
    "## 4. 特征优化建议函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d20356dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "优化建议器已定义\n"
     ]
    }
   ],
   "source": [
    "class FeatureOptimizationAdvisor:\n",
    "    \"\"\"\n",
    "    特征优化建议生成器\n",
    "    基于质量检查结果提供优化建议\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, quality_report, name='特征数据'):\n",
    "        \"\"\"\n",
    "        初始化建议器\n",
    "        \n",
    "        参数:\n",
    "        - quality_report: FeatureQualityChecker的质量报告\n",
    "        - name: 数据集名称\n",
    "        \"\"\"\n",
    "        self.quality_report = quality_report\n",
    "        self.name = name\n",
    "        self.suggestions = defaultdict(list)\n",
    "    \n",
    "    def analyze_and_suggest(self):\n",
    "        \"\"\"分析质量报告并生成优化建议\"\"\"\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(f\"特征优化建议: {self.name}\")\n",
    "        print(f\"{'='*80}\\n\")\n",
    "        \n",
    "        # 1. 缺失值处理建议\n",
    "        self._suggest_missing_values()\n",
    "        \n",
    "        # 2. 常量特征处理建议\n",
    "        self._suggest_constant_features()\n",
    "        \n",
    "        # 3. 低方差特征处理建议\n",
    "        self._suggest_low_variance()\n",
    "        \n",
    "        # 4. 无穷值处理建议\n",
    "        self._suggest_infinite_values()\n",
    "        \n",
    "        # 5. 重复样本处理建议\n",
    "        self._suggest_duplicates()\n",
    "        \n",
    "        # 6. 异常值处理建议\n",
    "        self._suggest_outliers()\n",
    "        \n",
    "        # 7. 高相关性处理建议\n",
    "        self._suggest_high_correlation()\n",
    "        \n",
    "        # 8. 特征工程建议\n",
    "        self._suggest_feature_engineering()\n",
    "        \n",
    "        # 9. 总结建议\n",
    "        self._print_summary()\n",
    "        \n",
    "        return self.suggestions\n",
    "    \n",
    "    def _suggest_missing_values(self):\n",
    "        \"\"\"缺失值处理建议\"\"\"\n",
    "        missing_data = self.quality_report.get('missing_values')\n",
    "        \n",
    "        if missing_data is None or len(missing_data) == 0:\n",
    "            print(\"[1] 缺失值处理\")\n",
    "            print(\"  状态: 良好 - 无缺失值\")\n",
    "            print()\n",
    "            return\n",
    "        \n",
    "        print(\"[1] 缺失值处理\")\n",
    "        print(f\"  发现 {len(missing_data)} 个特征存在缺失值\")\n",
    "        \n",
    "        high_missing = missing_data[missing_data['缺失率'] > 50]\n",
    "        medium_missing = missing_data[(missing_data['缺失率'] > 20) & (missing_data['缺失率'] <= 50)]\n",
    "        low_missing = missing_data[missing_data['缺失率'] <= 20]\n",
    "        \n",
    "        if len(high_missing) > 0:\n",
    "            print(f\"\\n  高缺失率特征 (>50%): {len(high_missing)}个\")\n",
    "            print(\"  建议: 考虑删除这些特征,或评估其业务价值后谨慎使用\")\n",
    "            self.suggestions['删除特征'].extend(high_missing['特征'].tolist())\n",
    "        \n",
    "        if len(medium_missing) > 0:\n",
    "            print(f\"\\n  中等缺失率特征 (20-50%): {len(medium_missing)}个\")\n",
    "            print(\"  建议: \")\n",
    "            print(\"    - 使用模型预测填充(如KNN、回归)\")\n",
    "            print(\"    - 创建缺失标识特征(is_missing)\")\n",
    "            print(\"    - 根据业务规则填充\")\n",
    "            self.suggestions['缺失值填充'].extend(medium_missing['特征'].tolist())\n",
    "        \n",
    "        if len(low_missing) > 0:\n",
    "            print(f\"\\n  低缺失率特征 (<20%): {len(low_missing)}个\")\n",
    "            print(\"  建议: \")\n",
    "            print(\"    - 使用中位数/均值填充(数值型)\")\n",
    "            print(\"    - 使用众数填充(分类型)\")\n",
    "            print(\"    - 使用0填充(计数型特征)\")\n",
    "            self.suggestions['简单填充'].extend(low_missing['特征'].tolist())\n",
    "        \n",
    "        print()\n",
    "    \n",
    "    def _suggest_constant_features(self):\n",
    "        \"\"\"常量特征处理建议\"\"\"\n",
    "        const_data = self.quality_report.get('constant_features')\n",
    "        \n",
    "        if const_data is None or len(const_data) == 0:\n",
    "            print(\"[2] 常量特征\")\n",
    "            print(\"  状态: 良好 - 无常量特征\")\n",
    "            print()\n",
    "            return\n",
    "        \n",
    "        print(\"[2] 常量特征\")\n",
    "        print(f\"  发现 {len(const_data)} 个常量特征\")\n",
    "        print(\"  建议: 直接删除,这些特征对模型无贡献\")\n",
    "        \n",
    "        self.suggestions['删除特征'].extend(const_data['特征'].tolist())\n",
    "        print()\n",
    "    \n",
    "    def _suggest_low_variance(self):\n",
    "        \"\"\"低方差特征处理建议\"\"\"\n",
    "        low_var_data = self.quality_report.get('low_variance_features')\n",
    "        \n",
    "        if low_var_data is None or len(low_var_data) == 0:\n",
    "            print(\"[3] 低方差特征\")\n",
    "            print(\"  状态: 良好 - 无低方差特征\")\n",
    "            print()\n",
    "            return\n",
    "        \n",
    "        print(\"[3] 低方差特征\")\n",
    "        print(f\"  发现 {len(low_var_data)} 个低方差特征\")\n",
    "        print(\"  建议: \")\n",
    "        print(\"    - 评估特征的业务意义\")\n",
    "        print(\"    - 方差过低可能区分度不足,考虑删除\")\n",
    "        print(\"    - 或尝试特征变换(log、sqrt等)增加方差\")\n",
    "        \n",
    "        self.suggestions['考虑删除'].extend(low_var_data['特征'].tolist()[:20])\n",
    "        print()\n",
    "    \n",
    "    def _suggest_infinite_values(self):\n",
    "        \"\"\"无穷值处理建议\"\"\"\n",
    "        inf_data = self.quality_report.get('infinite_values')\n",
    "        \n",
    "        if inf_data is None or len(inf_data) == 0:\n",
    "            print(\"[4] 无穷值\")\n",
    "            print(\"  状态: 良好 - 无无穷值\")\n",
    "            print()\n",
    "            return\n",
    "        \n",
    "        print(\"[4] 无穷值\")\n",
    "        print(f\"  发现 {len(inf_data)} 个特征包含无穷值\")\n",
    "        print(\"  建议: \")\n",
    "        print(\"    - 检查特征计算逻辑(除零错误)\")\n",
    "        print(\"    - 替换为极大/极小值\")\n",
    "        print(\"    - 或替换为NaN后填充\")\n",
    "        \n",
    "        self.suggestions['修复无穷值'].extend(inf_data['特征'].tolist())\n",
    "        print()\n",
    "    \n",
    "    def _suggest_duplicates(self):\n",
    "        \"\"\"重复样本处理建议\"\"\"\n",
    "        dup_data = self.quality_report.get('duplicates', {})\n",
    "        \n",
    "        print(\"[5] 重复样本\")\n",
    "        row_dup = dup_data.get('row_duplicates', 0)\n",
    "        id_dup = dup_data.get('id_duplicates', 0)\n",
    "        \n",
    "        if row_dup == 0 and id_dup == 0:\n",
    "            print(\"  状态: 良好 - 无重复样本\")\n",
    "            print()\n",
    "            return\n",
    "        \n",
    "        if id_dup > 0:\n",
    "            print(f\"  发现 {id_dup} 个重复ID\")\n",
    "            print(\"  建议: 检查数据来源,处理重复ID\")\n",
    "            self.suggestions['处理重复'].append(f'ID重复: {id_dup}个')\n",
    "        \n",
    "        if row_dup > 0:\n",
    "            print(f\"  发现 {row_dup} 个完全重复的样本\")\n",
    "            print(\"  建议: 删除重复样本,保留一个\")\n",
    "            self.suggestions['处理重复'].append(f'样本重复: {row_dup}个')\n",
    "        \n",
    "        print()\n",
    "    \n",
    "    def _suggest_outliers(self):\n",
    "        \"\"\"异常值处理建议\"\"\"\n",
    "        outlier_data = self.quality_report.get('outliers')\n",
    "        \n",
    "        if outlier_data is None or len(outlier_data) == 0:\n",
    "            print(\"[6] 异常值\")\n",
    "            print(\"  状态: 良好 - 无明显异常值\")\n",
    "            print()\n",
    "            return\n",
    "        \n",
    "        print(\"[6] 异常值\")\n",
    "        print(f\"  发现 {len(outlier_data)} 个特征包含异常值\")\n",
    "        \n",
    "        high_outlier = outlier_data[outlier_data['异常值占比(%)'] > 5]\n",
    "        \n",
    "        if len(high_outlier) > 0:\n",
    "            print(f\"\\n  高异常值占比特征 (>5%): {len(high_outlier)}个\")\n",
    "        \n",
    "        print(\"\\n  建议: \")\n",
    "        print(\"    - 方法1: Winsorization(限幅处理),替换为95/99分位数\")\n",
    "        print(\"    - 方法2: 对数变换,降低异常值影响\")\n",
    "        print(\"    - 方法3: 基于业务判断是否为真实异常\")\n",
    "        print(\"    - 方法4: 使用鲁棒的模型(如树模型)对异常值不敏感\")\n",
    "        \n",
    "        self.suggestions['异常值处理'].extend(outlier_data['特征'].tolist()[:20])\n",
    "        print()\n",
    "    \n",
    "    def _suggest_high_correlation(self):\n",
    "        \"\"\"高相关性处理建议\"\"\"\n",
    "        corr_data = self.quality_report.get('high_correlation')\n",
    "        \n",
    "        if corr_data is None or len(corr_data) == 0:\n",
    "            print(\"[7] 高相关性特征\")\n",
    "            print(\"  状态: 良好 - 无高相关性特征对\")\n",
    "            print()\n",
    "            return\n",
    "        \n",
    "        print(\"[7] 高相关性特征\")\n",
    "        print(f\"  发现 {len(corr_data)} 对高相关性特征\")\n",
    "        \n",
    "        very_high = corr_data[corr_data['相关系数'] >= 0.99]\n",
    "        \n",
    "        if len(very_high) > 0:\n",
    "            print(f\"\\n  极高相关性 (>=0.99): {len(very_high)}对\")\n",
    "            print(\"  这些特征对可能是冗余的\")\n",
    "        \n",
    "        print(\"\\n  建议: \")\n",
    "        print(\"    - 对于>=0.99的特征对,删除其中一个\")\n",
    "        print(\"    - 对于0.95-0.99的特征对,使用PCA降维\")\n",
    "        print(\"    - 或保留业务意义更强的特征\")\n",
    "        print(\"    - 使用特征选择算法(如LASSO)自动筛选\")\n",
    "        \n",
    "        # 提取需要处理的特征\n",
    "        redundant_features = set()\n",
    "        for _, row in very_high.iterrows():\n",
    "            redundant_features.add(row['特征2'])  # 保留特征1,删除特征2\n",
    "        \n",
    "        self.suggestions['高相关删除'].extend(list(redundant_features))\n",
    "        print()\n",
    "    \n",
    "    def _suggest_feature_engineering(self):\n",
    "        \"\"\"特征工程建议\"\"\"\n",
    "        print(\"[8] 特征工程建议\")\n",
    "        print(\"\\n  数据变换:\")\n",
    "        print(\"    - 对偏态分布特征进行log/sqrt变换\")\n",
    "        print(\"    - 对异常值多的特征进行Box-Cox变换\")\n",
    "        print(\"    - 标准化/归一化数值特征\")\n",
    "        print(\"\\n  特征交互:\")\n",
    "        print(\"    - 创建重要特征的交叉特征\")\n",
    "        print(\"    - 构建比率特征(如收入/支出)\")\n",
    "        print(\"    - 时间序列特征的滞后特征\")\n",
    "        print(\"\\n  特征选择:\")\n",
    "        print(\"    - 使用树模型的特征重要性筛选\")\n",
    "        print(\"    - 使用递归特征消除(RFE)\")\n",
    "        print(\"    - 使用LASSO正则化\")\n",
    "        print()\n",
    "    \n",
    "    def _print_summary(self):\n",
    "        \"\"\"打印优化建议总结\"\"\"\n",
    "        print(f\"{'='*80}\")\n",
    "        print(\"优化建议总结\")\n",
    "        print(f\"{'='*80}\\n\")\n",
    "        \n",
    "        # 统计需要处理的特征数\n",
    "        to_delete = set(self.suggestions['删除特征'] + self.suggestions['考虑删除'])\n",
    "        to_fill = set(self.suggestions['缺失值填充'] + self.suggestions['简单填充'])\n",
    "        to_fix_inf = set(self.suggestions['修复无穷值'])\n",
    "        to_handle_outlier = set(self.suggestions['异常值处理'])\n",
    "        to_handle_corr = set(self.suggestions['高相关删除'])\n",
    "        \n",
    "        print(f\"建议删除的特征数: {len(to_delete)}\")\n",
    "        print(f\"需要填充缺失值的特征数: {len(to_fill)}\")\n",
    "        print(f\"需要修复无穷值的特征数: {len(to_fix_inf)}\")\n",
    "        print(f\"需要处理异常值的特征数: {len(to_handle_outlier)}\")\n",
    "        print(f\"建议删除的高相关特征数: {len(to_handle_corr)}\")\n",
    "        \n",
    "        print(f\"\\n预计优化后保留特征数: {self.quality_report['basic_info']['特征数'] - len(to_delete) - len(to_handle_corr)}\")\n",
    "        \n",
    "        print(f\"\\n{'='*80}\\n\")\n",
    "    \n",
    "    def export_suggestions(self, output_path='./feature/optimization_suggestions.json'):\n",
    "        \"\"\"\n",
    "        导出优化建议到JSON文件\n",
    "        \n",
    "        参数:\n",
    "        - output_path: 输出文件路径\n",
    "        \"\"\"\n",
    "        # 转换为可序列化的格式\n",
    "        export_data = {\n",
    "            'dataset_name': self.name,\n",
    "            'generated_time': pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S'),\n",
    "            'suggestions': dict(self.suggestions),\n",
    "            'summary': {\n",
    "                'total_features': self.quality_report['basic_info']['特征数'],\n",
    "                'features_to_delete': len(set(self.suggestions['删除特征'] + self.suggestions['考虑删除'])),\n",
    "                'features_to_fill': len(set(self.suggestions['缺失值填充'] + self.suggestions['简单填充'])),\n",
    "                'features_to_fix_inf': len(set(self.suggestions['修复无穷值'])),\n",
    "                'features_to_handle_outlier': len(set(self.suggestions['异常值处理'])),\n",
    "                'features_to_handle_correlation': len(set(self.suggestions['高相关删除']))\n",
    "            }\n",
    "        }\n",
    "        \n",
    "        with open(output_path, 'w', encoding='utf-8') as f:\n",
    "            json.dump(export_data, f, ensure_ascii=False, indent=2)\n",
    "        \n",
    "        print(f\"优化建议已导出至: {output_path}\")\n",
    "    \n",
    "    def generate_optimization_code(self):\n",
    "        \"\"\"生成特征优化代码示例\"\"\"\n",
    "        print(f\"\\n{'='*80}\")\n",
    "        print(\"特征优化代码示例\")\n",
    "        print(f\"{'='*80}\\n\")\n",
    "        \n",
    "        code = \"\"\"\n",
    "# 特征优化代码模板\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "def optimize_features(df):\n",
    "    '''\n",
    "    基于质量检查结果优化特征\n",
    "    '''\n",
    "    df_optimized = df.copy()\n",
    "    \n",
    "    # 1. 删除常量特征和高缺失率特征\n",
    "    features_to_drop = [\n",
    "        # 在此处添加需要删除的特征列表\n",
    "    ]\n",
    "    df_optimized = df_optimized.drop(columns=features_to_drop, errors='ignore')\n",
    "    \n",
    "    # 2. 填充缺失值\n",
    "    # 数值型特征用中位数填充\n",
    "    numeric_cols = df_optimized.select_dtypes(include=[np.number]).columns\n",
    "    for col in numeric_cols:\n",
    "        if df_optimized[col].isnull().sum() > 0:\n",
    "            df_optimized[col].fillna(df_optimized[col].median(), inplace=True)\n",
    "    \n",
    "    # 3. 处理无穷值\n",
    "    df_optimized.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "    df_optimized.fillna(0, inplace=True)\n",
    "    \n",
    "    # 4. 异常值处理(Winsorization)\n",
    "    for col in numeric_cols:\n",
    "        q95 = df_optimized[col].quantile(0.95)\n",
    "        q05 = df_optimized[col].quantile(0.05)\n",
    "        df_optimized[col] = df_optimized[col].clip(lower=q05, upper=q95)\n",
    "    \n",
    "    # 5. 删除高相关特征\n",
    "    high_corr_features = [\n",
    "        # 在此处添加高相关特征列表\n",
    "    ]\n",
    "    df_optimized = df_optimized.drop(columns=high_corr_features, errors='ignore')\n",
    "    \n",
    "    # 6. 标准化(可选)\n",
    "    # scaler = StandardScaler()\n",
    "    # df_optimized[numeric_cols] = scaler.fit_transform(df_optimized[numeric_cols])\n",
    "    \n",
    "    return df_optimized\n",
    "\n",
    "# 使用示例\n",
    "# df_cleaned = optimize_features(df_original)\n",
    "\"\"\"\n",
    "        print(code)\n",
    "        print(f\"{'='*80}\\n\")\n",
    "\n",
    "print(\"优化建议器已定义\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a15524",
   "metadata": {},
   "source": [
    "## 5. 使用示例"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad9a791",
   "metadata": {},
   "source": [
    "### 5.1 加载所有特征文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "974233d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "发现 10 个特征文件\n",
      "================================================================================\n",
      "\n",
      "正在加载: AGET_PAY_features.pkl\n",
      "  - 形状: (530, 47)\n",
      "  - 文件大小: 0.21 MB\n",
      "  - 内存占用: 0.23 MB\n",
      "\n",
      "正在加载: ASSET_features.pkl\n",
      "  - 形状: (5624, 139)\n",
      "  - 文件大小: 5.99 MB\n",
      "  - 内存占用: 6.27 MB\n",
      "\n",
      "正在加载: CCD_TR_DTL_features.pkl\n",
      "  - 形状: (18, 48)\n",
      "  - 文件大小: 0.01 MB\n",
      "  - 内存占用: 0.01 MB\n",
      "\n",
      "正在加载: mb_pageview_dtl_features.pkl\n",
      "  - 形状: (2753, 503)\n",
      "  - 文件大小: 10.60 MB\n",
      "  - 内存占用: 10.71 MB\n",
      "\n",
      "正在加载: MB_TRNFLW_QRYTRNFLW_features.pkl\n",
      "  - 形状: (3128, 239)\n",
      "  - 文件大小: 5.77 MB\n",
      "  - 内存占用: 5.92 MB\n",
      "\n",
      "正在加载: NATURE_features.pkl\n",
      "  - 形状: (5975, 26)\n",
      "  - 文件大小: 1.21 MB\n",
      "  - 内存占用: 1.51 MB\n",
      "\n",
      "正在加载: PROD_HOLD_features.pkl\n",
      "  - 形状: (5741, 41)\n",
      "  - 文件大小: 1.66 MB\n",
      "  - 内存占用: 1.95 MB\n",
      "\n",
      "正在加载: tr_aps_dtl_features.pkl\n",
      "  - 形状: (5616, 309)\n",
      "  - 文件大小: 13.29 MB\n",
      "  - 内存占用: 13.57 MB\n",
      "\n",
      "正在加载: TR_IBTF_features.pkl\n",
      "  - 形状: (2981, 86)\n",
      "  - 文件大小: 1.96 MB\n",
      "  - 内存占用: 2.11 MB\n",
      "\n",
      "正在加载: TR_TPAY_features.pkl\n",
      "  - 形状: (3595, 65)\n",
      "  - 文件大小: 1.76 MB\n",
      "  - 内存占用: 1.94 MB\n",
      "\n",
      "================================================================================\n",
      "成功加载 10 个特征文件\n",
      "\n",
      "特征文件汇总:\n",
      "                            特征文件   样本数   特征数  文件大小(MB)  内存占用(MB)\n",
      "0       mb_pageview_dtl_features  2753   502     10.60     10.71\n",
      "1            tr_aps_dtl_features  5616   308     13.29     13.57\n",
      "2   MB_TRNFLW_QRYTRNFLW_features  3128   238      5.77      5.92\n",
      "3                 ASSET_features  5624   138      5.99      6.27\n",
      "4               TR_IBTF_features  2981    85      1.96      2.11\n",
      "5               TR_TPAY_features  3595    64      1.76      1.94\n",
      "6            CCD_TR_DTL_features    18    47      0.01      0.01\n",
      "7              AGET_PAY_features   530    46      0.21      0.23\n",
      "8             PROD_HOLD_features  5741    40      1.66      1.95\n",
      "9                NATURE_features  5975    25      1.21      1.51\n",
      "10                            总计     -  1493     42.46     44.22\n"
     ]
    }
   ],
   "source": [
    "# 加载所有特征文件\n",
    "features_dict = loader.load_all_features()\n",
    "\n",
    "# 查看特征文件汇总\n",
    "summary_df = loader.get_feature_summary()\n",
    "print(\"\\n特征文件汇总:\")\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babd9d7a",
   "metadata": {},
   "source": [
    "### 5.2 单个特征文件质量检查示例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9e0671d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "################################################################################\n",
      "# 特征质量全面检查: tr_aps_dtl_features\n",
      "################################################################################\n",
      "\n",
      "\n",
      "================================================================================\n",
      "基础信息检查: tr_aps_dtl_features\n",
      "================================================================================\n",
      "样本数: 5,616\n",
      "特征数: 308\n",
      "总列数: 309\n",
      "内存占用(MB): 13.57\n",
      "\n",
      "================================================================================\n",
      "缺失值检查\n",
      "================================================================================\n",
      "\n",
      "无缺失值\n",
      "\n",
      "================================================================================\n",
      "常量特征检查\n",
      "================================================================================\n",
      "\n",
      "无常量特征\n",
      "\n",
      "================================================================================\n",
      "低方差特征检查 (阈值: 0.01)\n",
      "================================================================================\n",
      "\n",
      "发现 210 个低方差特征\n",
      "                           特征           方差    标准化方差\n",
      "               aps_in_30d_min 4.638416e+07 0.000186\n",
      "               aps_in_60d_min 4.676987e+07 0.000187\n",
      "aps_weekday_weekend_amt_ratio 8.142352e+16 0.000204\n",
      "               aps_in_90d_min 5.363016e+07 0.000215\n",
      "               aps_all_3d_std 9.217741e+08 0.000220\n",
      "            aps_in_15d_median 2.193157e+09 0.000221\n",
      "     aps_first_last_amt_ratio 9.008496e+16 0.000225\n",
      "     aps_in_out_cnt_ratio_15d 7.880792e+00 0.000233\n",
      "               aps_all_1d_min 1.969528e+09 0.000234\n",
      "           aps_net_inflow_30d 1.719655e+10 0.000237\n",
      "              aps_all_1d_mean 2.002410e+09 0.000238\n",
      "              aps_all_60d_min 6.251537e+07 0.000250\n",
      "              aps_all_30d_min 6.238840e+07 0.000250\n",
      "     aps_in_out_amt_ratio_60d 1.632747e+08 0.000251\n",
      "               aps_all_3d_min 3.078198e+08 0.000257\n",
      "               aps_all_1d_max 2.170998e+09 0.000258\n",
      "              aps_all_90d_min 6.893695e+07 0.000276\n",
      "     aps_in_out_amt_ratio_90d 3.453871e+08 0.000289\n",
      "                aps_in_7d_sum 3.802936e+10 0.000290\n",
      "                aps_in_7d_min 2.457348e+09 0.000292\n",
      "\n",
      "================================================================================\n",
      "无穷值检查\n",
      "================================================================================\n",
      "\n",
      "无无穷值\n",
      "\n",
      "================================================================================\n",
      "重复样本检查\n",
      "================================================================================\n",
      "\n",
      "ID列重复数: 0\n",
      "完全重复的样本数: 352\n",
      "\n",
      "================================================================================\n",
      "异常值检查 (3-sigma原则)\n",
      "================================================================================\n",
      "\n",
      "发现 265 个特征包含异常值(前20个):\n",
      "                          特征  异常值数量  异常值占比(%)\n",
      "       aps_in_active_days_7d    224      3.99\n",
      "        aps_amt_ratio_7d_60d    212      3.77\n",
      "      aps_in_active_days_30d    193      3.44\n",
      "      aps_in_active_days_15d    192      3.42\n",
      "aps_max_consecutive_days_30d    191      3.40\n",
      "      aps_in_active_days_60d    188      3.35\n",
      "     aps_large_txn_ratio_15d    187      3.33\n",
      "      aps_in_active_days_90d    184      3.28\n",
      "      aps_large_txn_ratio_7d    164      2.92\n",
      "     aps_large_txn_ratio_30d    163      2.90\n",
      "     aps_large_txn_ratio_90d    160      2.85\n",
      "     aps_large_txn_ratio_60d    155      2.76\n",
      "aps_max_consecutive_days_60d    154      2.74\n",
      "        aps_interval_min_30d    151      2.69\n",
      "           aps_weekday_6_cnt    150      2.67\n",
      "aps_max_consecutive_days_90d    150      2.67\n",
      "           aps_out_15d_count    143      2.55\n",
      "       aps_outlier_ratio_30d    142      2.53\n",
      "        aps_top1_cod_cnt_60d    141      2.51\n",
      "             aps_weekend_cnt    141      2.51\n",
      "\n",
      "================================================================================\n",
      "高相关性检查 (阈值: 0.95)\n",
      "================================================================================\n",
      "\n",
      "计算 308 个数值特征的相关系数...\n",
      "\n",
      "发现 292 对高相关性特征(前20对):\n",
      "                特征1                   特征2  相关系数\n",
      "aps_active_days_60d aps_activity_rate_60d   1.0\n",
      "  aps_all_15d_count aps_daily_txn_avg_15d   1.0\n",
      "  aps_all_30d_count aps_daily_txn_avg_30d   1.0\n",
      "aps_active_days_15d aps_activity_rate_15d   1.0\n",
      "    aps_all_30d_sum    aps_last_month_amt   1.0\n",
      "  aps_txn_count_60d aps_daily_txn_avg_60d   1.0\n",
      "   aps_txn_count_7d  aps_daily_txn_avg_7d   1.0\n",
      "aps_active_days_90d aps_activity_rate_90d   1.0\n",
      " aps_active_days_7d  aps_activity_rate_7d   1.0\n",
      "  aps_txn_count_90d aps_daily_txn_avg_90d   1.0\n",
      "  aps_all_30d_count     aps_txn_count_30d   1.0\n",
      "  aps_all_60d_count     aps_txn_count_60d   1.0\n",
      "  aps_all_15d_count     aps_txn_count_15d   1.0\n",
      "  aps_all_60d_count aps_daily_txn_avg_60d   1.0\n",
      "aps_active_days_30d aps_activity_rate_30d   1.0\n",
      "  aps_txn_count_30d aps_daily_txn_avg_30d   1.0\n",
      "   aps_all_7d_count  aps_daily_txn_avg_7d   1.0\n",
      "   aps_all_7d_count      aps_txn_count_7d   1.0\n",
      "  aps_all_90d_count     aps_txn_count_90d   1.0\n",
      "  aps_all_90d_count aps_daily_txn_avg_90d   1.0\n",
      "\n",
      "################################################################################\n",
      "# 质量检查完成\n",
      "################################################################################\n",
      "\n",
      "\n",
      "发现 292 对高相关性特征(前20对):\n",
      "                特征1                   特征2  相关系数\n",
      "aps_active_days_60d aps_activity_rate_60d   1.0\n",
      "  aps_all_15d_count aps_daily_txn_avg_15d   1.0\n",
      "  aps_all_30d_count aps_daily_txn_avg_30d   1.0\n",
      "aps_active_days_15d aps_activity_rate_15d   1.0\n",
      "    aps_all_30d_sum    aps_last_month_amt   1.0\n",
      "  aps_txn_count_60d aps_daily_txn_avg_60d   1.0\n",
      "   aps_txn_count_7d  aps_daily_txn_avg_7d   1.0\n",
      "aps_active_days_90d aps_activity_rate_90d   1.0\n",
      " aps_active_days_7d  aps_activity_rate_7d   1.0\n",
      "  aps_txn_count_90d aps_daily_txn_avg_90d   1.0\n",
      "  aps_all_30d_count     aps_txn_count_30d   1.0\n",
      "  aps_all_60d_count     aps_txn_count_60d   1.0\n",
      "  aps_all_15d_count     aps_txn_count_15d   1.0\n",
      "  aps_all_60d_count aps_daily_txn_avg_60d   1.0\n",
      "aps_active_days_30d aps_activity_rate_30d   1.0\n",
      "  aps_txn_count_30d aps_daily_txn_avg_30d   1.0\n",
      "   aps_all_7d_count  aps_daily_txn_avg_7d   1.0\n",
      "   aps_all_7d_count      aps_txn_count_7d   1.0\n",
      "  aps_all_90d_count     aps_txn_count_90d   1.0\n",
      "  aps_all_90d_count aps_daily_txn_avg_90d   1.0\n",
      "\n",
      "################################################################################\n",
      "# 质量检查完成\n",
      "################################################################################\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 选择一个特征文件进行详细检查\n",
    "# 可以修改这里的名称来检查不同的特征文件\n",
    "feature_name = 'tr_aps_dtl_features'  # 活期交易特征\n",
    "\n",
    "if feature_name in features_dict:\n",
    "    df_check = features_dict[feature_name]\n",
    "    \n",
    "    # 创建质量检查器\n",
    "    checker = FeatureQualityChecker(df_check, name=feature_name)\n",
    "    \n",
    "    # 运行所有质量检查\n",
    "    quality_report = checker.run_all_checks()\n",
    "    \n",
    "else:\n",
    "    print(f\"未找到特征文件: {feature_name}\")\n",
    "    print(f\"可用的特征文件: {list(features_dict.keys())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a030f218",
   "metadata": {},
   "source": [
    "### 5.3 可视化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3dc15cf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建可视化器\n",
    "visualizer = FeatureVisualizer(df_check, name=feature_name)\n",
    "\n",
    "# 1. 质量检查摘要图\n",
    "visualizer.plot_quality_summary(quality_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "02b1aeef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "无缺失值,跳过可视化\n"
     ]
    }
   ],
   "source": [
    "# 2. 缺失值可视化\n",
    "visualizer.plot_missing_values(top_n=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b5bf2786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. 特征分布可视化(随机抽取12个特征)\n",
    "visualizer.plot_feature_distribution(sample_size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f6d3c606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. 异常值箱线图\n",
    "visualizer.plot_outlier_boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9a760684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5. 特征重要性(基于方差)\n",
    "visualizer.plot_feature_importance_by_variance(top_n=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "38907ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "计算 30 个特征的相关系数...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. 相关性热力图\n",
    "visualizer.plot_correlation_heatmap(top_n=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f2f9d1",
   "metadata": {},
   "source": [
    "### 5.4 生成优化建议"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4bafe4f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================================================================\n",
      "特征优化建议: tr_aps_dtl_features\n",
      "================================================================================\n",
      "\n",
      "[1] 缺失值处理\n",
      "  状态: 良好 - 无缺失值\n",
      "\n",
      "[2] 常量特征\n",
      "  状态: 良好 - 无常量特征\n",
      "\n",
      "[3] 低方差特征\n",
      "  发现 210 个低方差特征\n",
      "  建议: \n",
      "    - 评估特征的业务意义\n",
      "    - 方差过低可能区分度不足,考虑删除\n",
      "    - 或尝试特征变换(log、sqrt等)增加方差\n",
      "\n",
      "[4] 无穷值\n",
      "  状态: 良好 - 无无穷值\n",
      "\n",
      "[5] 重复样本\n",
      "  发现 352 个完全重复的样本\n",
      "  建议: 删除重复样本,保留一个\n",
      "\n",
      "[6] 异常值\n",
      "  发现 265 个特征包含异常值\n",
      "\n",
      "  建议: \n",
      "    - 方法1: Winsorization(限幅处理),替换为95/99分位数\n",
      "    - 方法2: 对数变换,降低异常值影响\n",
      "    - 方法3: 基于业务判断是否为真实异常\n",
      "    - 方法4: 使用鲁棒的模型(如树模型)对异常值不敏感\n",
      "\n",
      "[7] 高相关性特征\n",
      "  发现 292 对高相关性特征\n",
      "\n",
      "  极高相关性 (>=0.99): 38对\n",
      "  这些特征对可能是冗余的\n",
      "\n",
      "  建议: \n",
      "    - 对于>=0.99的特征对,删除其中一个\n",
      "    - 对于0.95-0.99的特征对,使用PCA降维\n",
      "    - 或保留业务意义更强的特征\n",
      "    - 使用特征选择算法(如LASSO)自动筛选\n",
      "\n",
      "[8] 特征工程建议\n",
      "\n",
      "  数据变换:\n",
      "    - 对偏态分布特征进行log/sqrt变换\n",
      "    - 对异常值多的特征进行Box-Cox变换\n",
      "    - 标准化/归一化数值特征\n",
      "\n",
      "  特征交互:\n",
      "    - 创建重要特征的交叉特征\n",
      "    - 构建比率特征(如收入/支出)\n",
      "    - 时间序列特征的滞后特征\n",
      "\n",
      "  特征选择:\n",
      "    - 使用树模型的特征重要性筛选\n",
      "    - 使用递归特征消除(RFE)\n",
      "    - 使用LASSO正则化\n",
      "\n",
      "================================================================================\n",
      "优化建议总结\n",
      "================================================================================\n",
      "\n",
      "建议删除的特征数: 20\n",
      "需要填充缺失值的特征数: 0\n",
      "需要修复无穷值的特征数: 0\n",
      "需要处理异常值的特征数: 20\n",
      "建议删除的高相关特征数: 30\n",
      "\n",
      "预计优化后保留特征数: 258\n",
      "\n",
      "================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 创建优化建议器\n",
    "advisor = FeatureOptimizationAdvisor(quality_report, name=feature_name)\n",
    "\n",
    "# 生成优化建议\n",
    "suggestions = advisor.analyze_and_suggest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40f58fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导出优化建议到JSON\n",
    "advisor.export_suggestions(output_path=f'./feature/{feature_name}_optimization_suggestions.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d919931",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成优化代码示例\n",
    "advisor.generate_optimization_code()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef42cf65",
   "metadata": {},
   "source": [
    "### 5.5 批量检查所有特征文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "861a2238",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 批量检查所有特征文件\n",
    "all_summaries = []\n",
    "\n",
    "for name, df in features_dict.items():\n",
    "    print(f\"\\n{'#'*80}\")\n",
    "    print(f\"正在检查: {name}\")\n",
    "    print(f\"{'#'*80}\")\n",
    "    \n",
    "    # 创建检查器\n",
    "    checker = FeatureQualityChecker(df, name=name)\n",
    "    \n",
    "    # 运行检查\n",
    "    report = checker.run_all_checks()\n",
    "    \n",
    "    # 获取摘要\n",
    "    summary = checker.get_summary_report()\n",
    "    all_summaries.append(summary)\n",
    "    \n",
    "    # 清理内存\n",
    "    del checker\n",
    "    gc.collect()\n",
    "\n",
    "# 生成总体质量报告\n",
    "print(\"\\n\\n\" + \"=\"*80)\n",
    "print(\"全部特征文件质量检查汇总\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "summary_df = pd.DataFrame(all_summaries)\n",
    "print(summary_df.to_string(index=False))\n",
    "\n",
    "# 保存汇总报告\n",
    "summary_df.to_csv('./feature/quality_summary_report.csv', index=False, encoding='utf-8-sig')\n",
    "print(\"\\n汇总报告已保存至: ./feature/quality_summary_report.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3589cdef",
   "metadata": {},
   "source": [
    "## 6. 使用说明\n",
    "\n",
    "### 工作流程\n",
    "\n",
    "1. **加载特征文件**: 使用`FeatureLoader`批量加载所有pkl特征文件\n",
    "2. **质量检查**: 使用`FeatureQualityChecker`进行全面质量检查\n",
    "3. **可视化分析**: 使用`FeatureVisualizer`生成各种可视化图表\n",
    "4. **优化建议**: 使用`FeatureOptimizationAdvisor`获取具体优化建议\n",
    "\n",
    "### 主要功能\n",
    "\n",
    "#### FeatureLoader (特征加载器)\n",
    "- `load_single_feature()`: 加载单个pkl文件\n",
    "- `load_all_features()`: 批量加载所有特征文件\n",
    "- `get_feature_summary()`: 获取特征文件汇总信息\n",
    "\n",
    "#### FeatureQualityChecker (质量检查器)\n",
    "- `check_basic_info()`: 基础信息检查\n",
    "- `check_missing_values()`: 缺失值检查\n",
    "- `check_constant_features()`: 常量特征检查\n",
    "- `check_low_variance_features()`: 低方差特征检查\n",
    "- `check_infinite_values()`: 无穷值检查\n",
    "- `check_duplicates()`: 重复样本检查\n",
    "- `check_outliers()`: 异常值检查\n",
    "- `check_correlation()`: 高相关性检查\n",
    "- `run_all_checks()`: 运行所有检查\n",
    "\n",
    "#### FeatureVisualizer (可视化工具)\n",
    "- `plot_missing_values()`: 缺失值分布图\n",
    "- `plot_feature_distribution()`: 特征分布直方图\n",
    "- `plot_correlation_heatmap()`: 相关性热力图\n",
    "- `plot_outlier_boxplot()`: 异常值箱线图\n",
    "- `plot_feature_importance_by_variance()`: 基于方差的特征重要性\n",
    "- `plot_quality_summary()`: 质量检查摘要图\n",
    "\n",
    "#### FeatureOptimizationAdvisor (优化建议器)\n",
    "- `analyze_and_suggest()`: 分析并生成优化建议\n",
    "- `export_suggestions()`: 导出建议到JSON文件\n",
    "- `generate_optimization_code()`: 生成优化代码模板\n",
    "\n",
    "### 自定义使用\n",
    "\n",
    "可以根据需要修改以下参数:\n",
    "- 缺失率阈值: `check_missing_values(threshold=0.5)`\n",
    "- 低方差阈值: `check_low_variance_features(threshold=0.01)`\n",
    "- 异常值标准: `check_outliers(n_std=3)`\n",
    "- 相关系数阈值: `check_correlation(threshold=0.95)`\n",
    "\n",
    "### 输出文件\n",
    "\n",
    "- `quality_summary_report.csv`: 全部特征文件质量汇总\n",
    "- `{feature_name}_optimization_suggestions.json`: 优化建议JSON文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d73fa299",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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